Predictive Models for Opioid Use Disorder Using Genomic, Social, and Clinical Factors
Predictive Models for Opioid Use Disorder Using Genomic, Social, and Clinical Factors
使用基因组、社会和临床因素的阿片类药物使用障碍的预测模型
批准号：
10797165
负责人：
Rodney Allanigue Gabriel
金额：
$19.75万
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
项目状态：
未结题
起止时间：
2023-09-15 至 2025-08-31
关键词：
AdultAffectAgeBody mass indexChronicClassificationClinicalClinical DataCodeCommunitiesControl GroupsCopy Number PolymorphismDataData SetDatabasesDependenceDevelopmentDiagnosisEnrollmentEnsureEquityEthnic OriginExposure toFamilyFibromyalgiaFutureGenderGenetic MarkersGenomicsGeographyIndividualMachine LearningMedicalMetadataMethodsMinorityModelingOutcomeOutputPainPatientsPerformancePersonsPharmaceutical PreparationsPopulationPopulation HeterogeneityPopulations at RiskPostoperative PainPrevalenceProceduresPublic HealthRaceRecording of previous eventsResearchRiskSamplingSingle Nucleotide PolymorphismSocial EnvironmentSocioeconomic StatusSubstance Use DisorderTechniquesTranslationsUnderrepresented PopulationsUnited StatesVariantVisualizationaddictionchronic painchronic painful conditionclinical careclinical decision supportclinically relevantgenome sequencinggenome wide association studygenomic biomarkergenomic datagenomic profileshealth equalityhealth equityhigh riskillicit opioidimprovedinsightinterestmachine learning modelmachine learning predictionmultiple data typesneural networknovelnovel markeropioid abuseopioid epidemicopioid mortalityopioid therapyopioid useopioid use disorderoutcome predictionpainful neuropathypatient populationpatient stratificationpredictive modelingprescription opioidprescription pain relieverrisk prediction modelrisk stratificationsexsocialsocial factorstime usetoolvectorwhole genome
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PROJECT SUMMARY / ABSTRACT
The opioid crisis is a major public health problem in the United States. Over the past two decades, opioid use
and abuse have increased dramatically, with over 5 million people in the United States using prescription
analgesics without medical need or prescription. This has resulted in a significant increase in opioid-related
deaths and addiction rates, with the crisis having a profound impact on individuals, families, and communities.
The proposal aims to develop machine learning-based predictive models for opioid use disorder (OUD)
leveraging genomic, social, and clinical factors. The project will utilize the diverse and equitable AllOfUs
database to identify novel genomic markers associated with OUD in patients with and without co-existing pain
conditions. A significant advantage of the AllOfUs database is the diversity of the patient population and clinical
samples – over 50% of the population is considered underrepresented. This will be achieved through genome-
wide association analysis to identify novel single nucleotide variants, copy number variants, and/or structural
variants. The project will also use machine learning techniques to develop predictive models that classify the risk
of OUD, integrating various data types such as clinical factors, social factors, and genomic data. The project
aims to identify key features that aid in the development of improved models for predicting the risk of OUD.
The first specific aim of the proposal is to identify associations between genomic profiles and OUD. The project
will focus on patients with or without co-existing pain conditions and identify novel genetic markers associated
with OUD in each of these unique patient populations.
The second specific aim is to develop predictive models using machine learning techniques to classify the risk
of OUD. The models will integrate social, clinical, and genomic data to provide clinicians with a tool to risk stratify
their patients.
The project aims to develop robust machine learning-based models predicting OUD and visualize the individual
features' impacts on model performance to provide understanding of which factors are most impactful to
predicting the outcome.
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